As set forth in commonly assigned U.S. Pat. Nos. 5,245,337, 5,293,164, and 5,592,667, a multi-dimensional approach has been developed for transforming an unstructured information system into a structured information system. This approach addresses the unique properties of multiple information source systems, including database systems, from an information point of view. In particular, this new methodology attempts to unify the two fields of information theory and database by combining the encoding compression theory and the database theory for general data manipulations into a general information manipulation theory with respect to multi-dimensional information space.
Broadly, multiple information sources are described by different information variables, each corresponding to one information source or information stream. Information manipulations are primarily index manipulations which are, in general, more efficient than non-index manipulations of the same number. The only non-index manipulations are carried out at leaf nodes where unique data values are stored. Therefore, the non-index manipulations are minimized. As a further aspect of this approach, a structured information system or database is built by taking into account information relations between different sets of data in the database. Such relations between neighboring nodes are easily analyzed and presented on-line because they are built into the structure. Relations between nodes that are not neighbors are not explicitly built into the existing structure. On-line analysis of these relations requires efficient information manipulations in main memory.
The approach models multiple information sources as different information variables, wherein each variable corresponds to one information source or information stream. In accordance with the methodology, information variables at leaf nodes of an associative memory database structure assume unique data values. The resulting structured database makes it easy to obtain statistical information about the data stored in the database. However, only a limited amount of statistic information can be readily presented once a given tree structure is built. For example, whereas it is trivial to show double patterns formed from two leaf nodes which happen to be the two child nodes of the same double pattern internal node in an existing tree structure, it is non-trivial to show similar double patterns formed from two leaf nodes that have an immediate common ancestor node which is not a double-pattern node in the existing tree structure.